Overview

Dataset statistics

Number of variables26
Number of observations194
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory40.9 KiB
Average record size in memory216.0 B

Variable types

Numeric16
Categorical10

Alerts

fuel-type is highly imbalanced (52.1%)Imbalance
engine-location is highly imbalanced (88.5%)Imbalance
num-of-cylinders is highly imbalanced (58.0%)Imbalance
symboling has 65 (33.5%) zerosZeros

Reproduction

Analysis started2024-05-28 09:25:34.978528
Analysis finished2024-05-28 09:26:18.588845
Duration43.61 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

symboling
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.78865979
Minimum-2
Maximum3
Zeros65
Zeros (%)33.5%
Negative23
Negative (%)11.9%
Memory size3.0 KiB
2024-05-28T14:56:18.732433image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-1
Q10
median1
Q32
95-th percentile3
Maximum3
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2008957
Coefficient of variation (CV)1.5227044
Kurtosis-0.52789383
Mean0.78865979
Median Absolute Deviation (MAD)1
Skewness0.21553879
Sum153
Variance1.4421505
MonotonicityNot monotonic
2024-05-28T14:56:18.906974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 65
33.5%
1 54
27.8%
2 31
16.0%
3 21
 
10.8%
-1 20
 
10.3%
-2 3
 
1.5%
ValueCountFrequency (%)
-2 3
 
1.5%
-1 20
 
10.3%
0 65
33.5%
1 54
27.8%
2 31
16.0%
3 21
 
10.8%
ValueCountFrequency (%)
3 21
 
10.8%
2 31
16.0%
1 54
27.8%
0 65
33.5%
-1 20
 
10.3%
-2 3
 
1.5%

normalized-losses
Real number (ℝ)

Distinct49
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.10309
Minimum65
Maximum256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:19.110440image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile75.95
Q1101
median115
Q3137
95-th percentile168
Maximum256
Range191
Interquartile range (IQR)36

Descriptive statistics

Standard deviation31.49981
Coefficient of variation (CV)0.2622731
Kurtosis1.6285637
Mean120.10309
Median Absolute Deviation (MAD)19.5
Skewness0.97072775
Sum23300
Variance992.23802
MonotonicityNot monotonic
2024-05-28T14:56:19.345392image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
115 39
20.1%
161 11
 
5.7%
91 8
 
4.1%
150 7
 
3.6%
128 6
 
3.1%
104 6
 
3.1%
95 5
 
2.6%
168 5
 
2.6%
74 5
 
2.6%
102 5
 
2.6%
Other values (39) 97
50.0%
ValueCountFrequency (%)
65 5
2.6%
74 5
2.6%
77 1
 
0.5%
78 1
 
0.5%
81 2
 
1.0%
83 3
 
1.5%
85 4
2.1%
87 2
 
1.0%
89 2
 
1.0%
91 8
4.1%
ValueCountFrequency (%)
256 1
 
0.5%
231 1
 
0.5%
194 2
 
1.0%
192 2
 
1.0%
188 2
 
1.0%
186 1
 
0.5%
168 5
2.6%
164 2
 
1.0%
161 11
5.7%
158 2
 
1.0%

make
Categorical

Distinct22
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
toyota
27 
nissan
18 
mazda
17 
volkswagen
12 
subaru
12 
Other values (17)
108 

Length

Max length13
Median length11
Mean length6.4690722
Min length3

Characters and Unicode

Total characters1255
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowalfa-romero
2nd rowalfa-romero
3rd rowalfa-romero
4th rowaudi
5th rowaudi

Common Values

ValueCountFrequency (%)
toyota 27
13.9%
nissan 18
 
9.3%
mazda 17
 
8.8%
volkswagen 12
 
6.2%
subaru 12
 
6.2%
honda 12
 
6.2%
mitsubishi 11
 
5.7%
peugot 11
 
5.7%
volvo 11
 
5.7%
dodge 8
 
4.1%
Other values (12) 55
28.4%

Length

2024-05-28T14:56:19.569667image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 27
13.9%
nissan 18
 
9.3%
mazda 17
 
8.8%
volkswagen 12
 
6.2%
subaru 12
 
6.2%
honda 12
 
6.2%
mitsubishi 11
 
5.7%
peugot 11
 
5.7%
volvo 11
 
5.7%
bmw 8
 
4.1%
Other values (12) 55
28.4%

Most occurring characters

ValueCountFrequency (%)
a 148
 
11.8%
o 139
 
11.1%
s 104
 
8.3%
t 87
 
6.9%
e 80
 
6.4%
u 71
 
5.7%
n 70
 
5.6%
i 61
 
4.9%
d 60
 
4.8%
m 54
 
4.3%
Other values (15) 381
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1244
99.1%
Dash Punctuation 11
 
0.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 148
 
11.9%
o 139
 
11.2%
s 104
 
8.4%
t 87
 
7.0%
e 80
 
6.4%
u 71
 
5.7%
n 70
 
5.6%
i 61
 
4.9%
d 60
 
4.8%
m 54
 
4.3%
Other values (14) 370
29.7%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1244
99.1%
Common 11
 
0.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 148
 
11.9%
o 139
 
11.2%
s 104
 
8.4%
t 87
 
7.0%
e 80
 
6.4%
u 71
 
5.7%
n 70
 
5.6%
i 61
 
4.9%
d 60
 
4.8%
m 54
 
4.3%
Other values (14) 370
29.7%
Common
ValueCountFrequency (%)
- 11
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1255
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 148
 
11.8%
o 139
 
11.1%
s 104
 
8.3%
t 87
 
6.9%
e 80
 
6.4%
u 71
 
5.7%
n 70
 
5.6%
i 61
 
4.9%
d 60
 
4.8%
m 54
 
4.3%
Other values (15) 381
30.4%

fuel-type
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
gas
174 
diesel
20 

Length

Max length6
Median length3
Mean length3.3092784
Min length3

Characters and Unicode

Total characters642
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgas
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 174
89.7%
diesel 20
 
10.3%

Length

2024-05-28T14:56:19.760537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T14:56:19.935547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
gas 174
89.7%
diesel 20
 
10.3%

Most occurring characters

ValueCountFrequency (%)
s 194
30.2%
g 174
27.1%
a 174
27.1%
e 40
 
6.2%
d 20
 
3.1%
i 20
 
3.1%
l 20
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 642
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 194
30.2%
g 174
27.1%
a 174
27.1%
e 40
 
6.2%
d 20
 
3.1%
i 20
 
3.1%
l 20
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 642
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 194
30.2%
g 174
27.1%
a 174
27.1%
e 40
 
6.2%
d 20
 
3.1%
i 20
 
3.1%
l 20
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 642
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 194
30.2%
g 174
27.1%
a 174
27.1%
e 40
 
6.2%
d 20
 
3.1%
i 20
 
3.1%
l 20
 
3.1%

aspiration
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
std
161 
turbo
33 

Length

Max length5
Median length3
Mean length3.3402062
Min length3

Characters and Unicode

Total characters648
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowstd
2nd rowstd
3rd rowstd
4th rowstd
5th rowstd

Common Values

ValueCountFrequency (%)
std 161
83.0%
turbo 33
 
17.0%

Length

2024-05-28T14:56:20.121800image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T14:56:20.296340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
std 161
83.0%
turbo 33
 
17.0%

Most occurring characters

ValueCountFrequency (%)
t 194
29.9%
s 161
24.8%
d 161
24.8%
u 33
 
5.1%
r 33
 
5.1%
b 33
 
5.1%
o 33
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 648
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 194
29.9%
s 161
24.8%
d 161
24.8%
u 33
 
5.1%
r 33
 
5.1%
b 33
 
5.1%
o 33
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 648
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 194
29.9%
s 161
24.8%
d 161
24.8%
u 33
 
5.1%
r 33
 
5.1%
b 33
 
5.1%
o 33
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 648
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 194
29.9%
s 161
24.8%
d 161
24.8%
u 33
 
5.1%
r 33
 
5.1%
b 33
 
5.1%
o 33
 
5.1%

num-of-doors
Categorical

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
four
112 
two
82 

Length

Max length4
Median length4
Mean length3.5773196
Min length3

Characters and Unicode

Total characters694
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtwo
2nd rowtwo
3rd rowtwo
4th rowfour
5th rowfour

Common Values

ValueCountFrequency (%)
four 112
57.7%
two 82
42.3%

Length

2024-05-28T14:56:20.469512image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T14:56:20.623101image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
four 112
57.7%
two 82
42.3%

Most occurring characters

ValueCountFrequency (%)
o 194
28.0%
f 112
16.1%
u 112
16.1%
r 112
16.1%
t 82
11.8%
w 82
11.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 694
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 194
28.0%
f 112
16.1%
u 112
16.1%
r 112
16.1%
t 82
11.8%
w 82
11.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 694
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 194
28.0%
f 112
16.1%
u 112
16.1%
r 112
16.1%
t 82
11.8%
w 82
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 194
28.0%
f 112
16.1%
u 112
16.1%
r 112
16.1%
t 82
11.8%
w 82
11.8%

body-style
Categorical

Distinct5
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
sedan
93 
hatchback
64 
wagon
24 
hardtop
 
8
convertible
 
5

Length

Max length11
Median length5
Mean length6.556701
Min length5

Characters and Unicode

Total characters1272
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowconvertible
2nd rowconvertible
3rd rowhatchback
4th rowsedan
5th rowsedan

Common Values

ValueCountFrequency (%)
sedan 93
47.9%
hatchback 64
33.0%
wagon 24
 
12.4%
hardtop 8
 
4.1%
convertible 5
 
2.6%

Length

2024-05-28T14:56:20.807784image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T14:56:20.994602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
sedan 93
47.9%
hatchback 64
33.0%
wagon 24
 
12.4%
hardtop 8
 
4.1%
convertible 5
 
2.6%

Most occurring characters

ValueCountFrequency (%)
a 253
19.9%
h 136
10.7%
c 133
10.5%
n 122
9.6%
e 103
8.1%
d 101
 
7.9%
s 93
 
7.3%
t 77
 
6.1%
b 69
 
5.4%
k 64
 
5.0%
Other values (8) 121
9.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1272
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 253
19.9%
h 136
10.7%
c 133
10.5%
n 122
9.6%
e 103
8.1%
d 101
 
7.9%
s 93
 
7.3%
t 77
 
6.1%
b 69
 
5.4%
k 64
 
5.0%
Other values (8) 121
9.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1272
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 253
19.9%
h 136
10.7%
c 133
10.5%
n 122
9.6%
e 103
8.1%
d 101
 
7.9%
s 93
 
7.3%
t 77
 
6.1%
b 69
 
5.4%
k 64
 
5.0%
Other values (8) 121
9.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1272
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 253
19.9%
h 136
10.7%
c 133
10.5%
n 122
9.6%
e 103
8.1%
d 101
 
7.9%
s 93
 
7.3%
t 77
 
6.1%
b 69
 
5.4%
k 64
 
5.0%
Other values (8) 121
9.5%

drive-wheels
Categorical

Distinct3
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
fwd
116 
rwd
69 
4wd
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters582
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowrwd
2nd rowrwd
3rd rowrwd
4th rowfwd
5th row4wd

Common Values

ValueCountFrequency (%)
fwd 116
59.8%
rwd 69
35.6%
4wd 9
 
4.6%

Length

2024-05-28T14:56:21.184445image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T14:56:21.342041image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
fwd 116
59.8%
rwd 69
35.6%
4wd 9
 
4.6%

Most occurring characters

ValueCountFrequency (%)
w 194
33.3%
d 194
33.3%
f 116
19.9%
r 69
 
11.9%
4 9
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 573
98.5%
Decimal Number 9
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 194
33.9%
d 194
33.9%
f 116
20.2%
r 69
 
12.0%
Decimal Number
ValueCountFrequency (%)
4 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 573
98.5%
Common 9
 
1.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 194
33.9%
d 194
33.9%
f 116
20.2%
r 69
 
12.0%
Common
ValueCountFrequency (%)
4 9
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 582
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 194
33.3%
d 194
33.3%
f 116
19.9%
r 69
 
11.9%
4 9
 
1.5%

engine-location
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
front
191 
rear
 
3

Length

Max length5
Median length5
Mean length4.9845361
Min length4

Characters and Unicode

Total characters967
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfront
2nd rowfront
3rd rowfront
4th rowfront
5th rowfront

Common Values

ValueCountFrequency (%)
front 191
98.5%
rear 3
 
1.5%

Length

2024-05-28T14:56:21.503787image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T14:56:22.134032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
front 191
98.5%
rear 3
 
1.5%

Most occurring characters

ValueCountFrequency (%)
r 197
20.4%
f 191
19.8%
o 191
19.8%
n 191
19.8%
t 191
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 967
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 197
20.4%
f 191
19.8%
o 191
19.8%
n 191
19.8%
t 191
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 967
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 197
20.4%
f 191
19.8%
o 191
19.8%
n 191
19.8%
t 191
19.8%
e 3
 
0.3%
a 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 967
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 197
20.4%
f 191
19.8%
o 191
19.8%
n 191
19.8%
t 191
19.8%
e 3
 
0.3%
a 3
 
0.3%

wheel-base
Real number (ℝ)

Distinct50
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.75
Minimum86.6
Maximum120.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:22.321729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum86.6
5-th percentile92.405
Q194.5
median97
Q3101.8
95-th percentile110.7
Maximum120.9
Range34.3
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation6.1245133
Coefficient of variation (CV)0.062020388
Kurtosis0.9632844
Mean98.75
Median Absolute Deviation (MAD)2.8
Skewness1.0515154
Sum19157.5
Variance37.509663
MonotonicityNot monotonic
2024-05-28T14:56:22.576710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94.5 21
 
10.8%
93.7 20
 
10.3%
95.7 13
 
6.7%
96.5 7
 
3.6%
97.3 7
 
3.6%
96.3 6
 
3.1%
104.3 6
 
3.1%
100.4 6
 
3.1%
107.9 6
 
3.1%
98.8 6
 
3.1%
Other values (40) 96
49.5%
ValueCountFrequency (%)
86.6 2
 
1.0%
88.4 1
 
0.5%
88.6 2
 
1.0%
89.5 3
 
1.5%
91.3 2
 
1.0%
93 1
 
0.5%
93.1 5
 
2.6%
93.3 1
 
0.5%
93.7 20
10.3%
94.5 21
10.8%
ValueCountFrequency (%)
120.9 1
 
0.5%
115.6 2
 
1.0%
114.2 4
2.1%
113 2
 
1.0%
112 1
 
0.5%
110 3
1.5%
109.1 5
2.6%
108 1
 
0.5%
107.9 6
3.1%
106.7 1
 
0.5%

length
Real number (ℝ)

Distinct73
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean173.8268
Minimum141.1
Maximum208.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:22.805138image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum141.1
5-th percentile157.03
Q1166.3
median172.5
Q3181.65
95-th percentile197.665
Maximum208.1
Range67
Interquartile range (IQR)15.35

Descriptive statistics

Standard deviation12.563542
Coefficient of variation (CV)0.072276208
Kurtosis-0.13814168
Mean173.8268
Median Absolute Deviation (MAD)6.85
Skewness0.19175129
Sum33722.4
Variance157.84259
MonotonicityNot monotonic
2024-05-28T14:56:23.041008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
157.3 15
 
7.7%
188.8 11
 
5.7%
171.7 7
 
3.6%
166.3 7
 
3.6%
186.7 7
 
3.6%
165.3 6
 
3.1%
177.8 6
 
3.1%
186.6 6
 
3.1%
172 5
 
2.6%
175.6 5
 
2.6%
Other values (63) 119
61.3%
ValueCountFrequency (%)
141.1 1
 
0.5%
144.6 2
 
1.0%
150 3
 
1.5%
155.9 3
 
1.5%
156.9 1
 
0.5%
157.1 1
 
0.5%
157.3 15
7.7%
157.9 1
 
0.5%
158.7 3
 
1.5%
158.8 1
 
0.5%
ValueCountFrequency (%)
208.1 1
 
0.5%
202.6 2
1.0%
199.6 2
1.0%
199.2 1
 
0.5%
198.9 4
2.1%
197 1
 
0.5%
193.8 1
 
0.5%
192.7 3
1.5%
191.7 1
 
0.5%
190.9 2
1.0%

width
Real number (ℝ)

Distinct42
Distinct (%)21.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.901546
Minimum60.3
Maximum72.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:23.269796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum60.3
5-th percentile63.6
Q164
median65.4
Q366.9
95-th percentile70.535
Maximum72.3
Range12
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation2.1756656
Coefficient of variation (CV)0.033013878
Kurtosis0.64100884
Mean65.901546
Median Absolute Deviation (MAD)1.4
Skewness0.95442799
Sum12784.9
Variance4.7335209
MonotonicityNot monotonic
2024-05-28T14:56:23.498382image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
63.8 24
 
12.4%
66.5 21
 
10.8%
65.4 15
 
7.7%
63.6 11
 
5.7%
68.4 10
 
5.2%
64.4 10
 
5.2%
64 9
 
4.6%
65.5 8
 
4.1%
65.2 6
 
3.1%
67.2 6
 
3.1%
Other values (32) 74
38.1%
ValueCountFrequency (%)
60.3 1
 
0.5%
62.5 1
 
0.5%
63.4 1
 
0.5%
63.6 11
5.7%
63.8 24
12.4%
63.9 3
 
1.5%
64 9
 
4.6%
64.1 2
 
1.0%
64.2 6
 
3.1%
64.4 10
5.2%
ValueCountFrequency (%)
72.3 1
 
0.5%
72 1
 
0.5%
71.7 3
1.5%
71.4 3
1.5%
70.9 1
 
0.5%
70.6 1
 
0.5%
70.5 1
 
0.5%
70.3 3
1.5%
69.6 2
1.0%
68.9 4
2.1%

height
Real number (ℝ)

Distinct49
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean53.814433
Minimum47.8
Maximum59.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:23.721921image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum47.8
5-th percentile49.7
Q152
median54.1
Q355.675
95-th percentile57.78
Maximum59.8
Range12
Interquartile range (IQR)3.675

Descriptive statistics

Standard deviation2.4514346
Coefficient of variation (CV)0.045553478
Kurtosis-0.43442428
Mean53.814433
Median Absolute Deviation (MAD)1.6
Skewness0.021312877
Sum10440
Variance6.0095315
MonotonicityNot monotonic
2024-05-28T14:56:23.965384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
50.8 14
 
7.2%
55.7 12
 
6.2%
52 10
 
5.2%
54.5 10
 
5.2%
55.5 9
 
4.6%
54.3 8
 
4.1%
56.7 8
 
4.1%
52.6 7
 
3.6%
54.1 7
 
3.6%
56.1 7
 
3.6%
Other values (39) 102
52.6%
ValueCountFrequency (%)
47.8 1
 
0.5%
48.8 2
 
1.0%
49.4 2
 
1.0%
49.6 4
 
2.1%
49.7 3
 
1.5%
50.2 2
 
1.0%
50.5 2
 
1.0%
50.6 5
 
2.6%
50.8 14
7.2%
51 1
 
0.5%
ValueCountFrequency (%)
59.8 2
 
1.0%
59.1 3
 
1.5%
58.7 4
2.1%
58.3 1
 
0.5%
57.5 3
 
1.5%
56.7 8
4.1%
56.5 2
 
1.0%
56.3 2
 
1.0%
56.2 3
 
1.5%
56.1 7
3.6%

curb-weight
Real number (ℝ)

Distinct161
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2537.9588
Minimum1488
Maximum4066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:24.193545image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1488
5-th percentile1896.5
Q12131
median2404
Q32894.25
95-th percentile3508.5
Maximum4066
Range2578
Interquartile range (IQR)763.25

Descriptive statistics

Standard deviation526.67032
Coefficient of variation (CV)0.20751729
Kurtosis0.059929611
Mean2537.9588
Median Absolute Deviation (MAD)365.5
Skewness0.77008839
Sum492364
Variance277381.63
MonotonicityNot monotonic
2024-05-28T14:56:24.440171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2385 4
 
2.1%
1989 3
 
1.5%
1918 3
 
1.5%
2275 3
 
1.5%
2191 2
 
1.0%
2535 2
 
1.0%
2024 2
 
1.0%
2414 2
 
1.0%
4066 2
 
1.0%
2300 2
 
1.0%
Other values (151) 169
87.1%
ValueCountFrequency (%)
1488 1
0.5%
1713 1
0.5%
1819 1
0.5%
1837 1
0.5%
1874 2
1.0%
1876 2
1.0%
1889 1
0.5%
1890 1
0.5%
1900 1
0.5%
1905 1
0.5%
ValueCountFrequency (%)
4066 2
1.0%
3950 1
0.5%
3900 1
0.5%
3770 1
0.5%
3750 1
0.5%
3740 1
0.5%
3715 1
0.5%
3685 1
0.5%
3515 1
0.5%
3505 1
0.5%

engine-type
Categorical

Distinct7
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
ohc
141 
ohcf
15 
ohcv
 
13
l
 
12
dohc
 
8
Other values (2)
 
5

Length

Max length5
Median length3
Mean length3.1134021
Min length1

Characters and Unicode

Total characters604
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowdohc
2nd rowdohc
3rd rowohcv
4th rowohc
5th rowohc

Common Values

ValueCountFrequency (%)
ohc 141
72.7%
ohcf 15
 
7.7%
ohcv 13
 
6.7%
l 12
 
6.2%
dohc 8
 
4.1%
rotor 4
 
2.1%
dohcv 1
 
0.5%

Length

2024-05-28T14:56:24.695180image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T14:56:24.901266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
ohc 141
72.7%
ohcf 15
 
7.7%
ohcv 13
 
6.7%
l 12
 
6.2%
dohc 8
 
4.1%
rotor 4
 
2.1%
dohcv 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
o 186
30.8%
h 178
29.5%
c 178
29.5%
f 15
 
2.5%
v 14
 
2.3%
l 12
 
2.0%
d 9
 
1.5%
r 8
 
1.3%
t 4
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 604
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 186
30.8%
h 178
29.5%
c 178
29.5%
f 15
 
2.5%
v 14
 
2.3%
l 12
 
2.0%
d 9
 
1.5%
r 8
 
1.3%
t 4
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 604
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 186
30.8%
h 178
29.5%
c 178
29.5%
f 15
 
2.5%
v 14
 
2.3%
l 12
 
2.0%
d 9
 
1.5%
r 8
 
1.3%
t 4
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 604
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 186
30.8%
h 178
29.5%
c 178
29.5%
f 15
 
2.5%
v 14
 
2.3%
l 12
 
2.0%
d 9
 
1.5%
r 8
 
1.3%
t 4
 
0.7%

num-of-cylinders
Categorical

IMBALANCE 

Distinct7
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
four
152 
six
20 
five
 
11
eight
 
5
two
 
4
Other values (2)
 
2

Length

Max length6
Median length4
Mean length3.9175258
Min length3

Characters and Unicode

Total characters760
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st rowfour
2nd rowfour
3rd rowsix
4th rowfour
5th rowfive

Common Values

ValueCountFrequency (%)
four 152
78.4%
six 20
 
10.3%
five 11
 
5.7%
eight 5
 
2.6%
two 4
 
2.1%
three 1
 
0.5%
twelve 1
 
0.5%

Length

2024-05-28T14:56:25.108227image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T14:56:25.302283image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
four 152
78.4%
six 20
 
10.3%
five 11
 
5.7%
eight 5
 
2.6%
two 4
 
2.1%
three 1
 
0.5%
twelve 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
f 163
21.4%
o 156
20.5%
r 153
20.1%
u 152
20.0%
i 36
 
4.7%
s 20
 
2.6%
x 20
 
2.6%
e 20
 
2.6%
v 12
 
1.6%
t 11
 
1.4%
Other values (4) 17
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 760
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 163
21.4%
o 156
20.5%
r 153
20.1%
u 152
20.0%
i 36
 
4.7%
s 20
 
2.6%
x 20
 
2.6%
e 20
 
2.6%
v 12
 
1.6%
t 11
 
1.4%
Other values (4) 17
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 760
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 163
21.4%
o 156
20.5%
r 153
20.1%
u 152
20.0%
i 36
 
4.7%
s 20
 
2.6%
x 20
 
2.6%
e 20
 
2.6%
v 12
 
1.6%
t 11
 
1.4%
Other values (4) 17
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 163
21.4%
o 156
20.5%
r 153
20.1%
u 152
20.0%
i 36
 
4.7%
s 20
 
2.6%
x 20
 
2.6%
e 20
 
2.6%
v 12
 
1.6%
t 11
 
1.4%
Other values (4) 17
 
2.2%

engine-size
Real number (ℝ)

Distinct41
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.52062
Minimum61
Maximum326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:25.516755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile90
Q197
median110
Q3140.75
95-th percentile205.1
Maximum326
Range265
Interquartile range (IQR)43.75

Descriptive statistics

Standard deviation42.101679
Coefficient of variation (CV)0.33541644
Kurtosis5.6587302
Mean125.52062
Median Absolute Deviation (MAD)18
Skewness2.0654552
Sum24351
Variance1772.5514
MonotonicityNot monotonic
2024-05-28T14:56:25.733075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
122 15
 
7.7%
92 15
 
7.7%
97 14
 
7.2%
98 14
 
7.2%
108 13
 
6.7%
90 12
 
6.2%
110 11
 
5.7%
109 8
 
4.1%
120 7
 
3.6%
141 7
 
3.6%
Other values (31) 78
40.2%
ValueCountFrequency (%)
61 1
 
0.5%
70 3
 
1.5%
79 1
 
0.5%
80 1
 
0.5%
90 12
6.2%
91 5
 
2.6%
92 15
7.7%
97 14
7.2%
98 14
7.2%
103 1
 
0.5%
ValueCountFrequency (%)
326 1
 
0.5%
308 1
 
0.5%
304 1
 
0.5%
258 2
 
1.0%
234 2
 
1.0%
209 3
1.5%
203 1
 
0.5%
194 3
1.5%
183 4
2.1%
181 6
3.1%

fuel-system
Categorical

Distinct7
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
mpfi
88 
2bbl
65 
idi
20 
1bbl
11 
spdi
 
6
Other values (2)
 
4

Length

Max length4
Median length4
Mean length3.8969072
Min length3

Characters and Unicode

Total characters756
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowmpfi
2nd rowmpfi
3rd rowmpfi
4th rowmpfi
5th rowmpfi

Common Values

ValueCountFrequency (%)
mpfi 88
45.4%
2bbl 65
33.5%
idi 20
 
10.3%
1bbl 11
 
5.7%
spdi 6
 
3.1%
4bbl 3
 
1.5%
spfi 1
 
0.5%

Length

2024-05-28T14:56:25.936043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-28T14:56:26.105775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
mpfi 88
45.4%
2bbl 65
33.5%
idi 20
 
10.3%
1bbl 11
 
5.7%
spdi 6
 
3.1%
4bbl 3
 
1.5%
spfi 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
b 158
20.9%
i 135
17.9%
p 95
12.6%
f 89
11.8%
m 88
11.6%
l 79
10.4%
2 65
8.6%
d 26
 
3.4%
1 11
 
1.5%
s 7
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 677
89.6%
Decimal Number 79
 
10.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b 158
23.3%
i 135
19.9%
p 95
14.0%
f 89
13.1%
m 88
13.0%
l 79
11.7%
d 26
 
3.8%
s 7
 
1.0%
Decimal Number
ValueCountFrequency (%)
2 65
82.3%
1 11
 
13.9%
4 3
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 677
89.6%
Common 79
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
b 158
23.3%
i 135
19.9%
p 95
14.0%
f 89
13.1%
m 88
13.0%
l 79
11.7%
d 26
 
3.8%
s 7
 
1.0%
Common
ValueCountFrequency (%)
2 65
82.3%
1 11
 
13.9%
4 3
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b 158
20.9%
i 135
17.9%
p 95
12.6%
f 89
11.8%
m 88
11.6%
l 79
10.4%
2 65
8.6%
d 26
 
3.4%
1 11
 
1.5%
s 7
 
0.9%

bore
Real number (ℝ)

Distinct36
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3246907
Minimum2.54
Maximum3.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:26.319156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2.54
5-th percentile2.9525
Q13.135
median3.31
Q33.58
95-th percentile3.78
Maximum3.94
Range1.4
Interquartile range (IQR)0.445

Descriptive statistics

Standard deviation0.274606
Coefficient of variation (CV)0.082595952
Kurtosis-0.80698564
Mean3.3246907
Median Absolute Deviation (MAD)0.26
Skewness0.053512361
Sum644.99
Variance0.075408453
MonotonicityNot monotonic
2024-05-28T14:56:26.556521image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.62 22
 
11.3%
3.19 20
 
10.3%
3.15 14
 
7.2%
3.03 12
 
6.2%
2.97 12
 
6.2%
3.31 11
 
5.7%
3.46 9
 
4.6%
3.78 8
 
4.1%
3.43 8
 
4.1%
2.91 7
 
3.6%
Other values (26) 71
36.6%
ValueCountFrequency (%)
2.54 1
 
0.5%
2.68 1
 
0.5%
2.91 7
3.6%
2.92 1
 
0.5%
2.97 12
6.2%
2.99 1
 
0.5%
3.01 5
2.6%
3.03 12
6.2%
3.05 6
3.1%
3.08 1
 
0.5%
ValueCountFrequency (%)
3.94 2
 
1.0%
3.8 2
 
1.0%
3.78 8
 
4.1%
3.76 1
 
0.5%
3.74 3
 
1.5%
3.7 5
 
2.6%
3.63 2
 
1.0%
3.62 22
11.3%
3.61 1
 
0.5%
3.58 6
 
3.1%

stroke
Real number (ℝ)

Distinct36
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2387113
Minimum2.07
Maximum4.17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:26.770533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2.07
5-th percentile2.64
Q13.11
median3.29
Q33.4
95-th percentile3.601
Maximum4.17
Range2.1
Interquartile range (IQR)0.29

Descriptive statistics

Standard deviation0.30791778
Coefficient of variation (CV)0.095074165
Kurtosis2.3562476
Mean3.2387113
Median Absolute Deviation (MAD)0.14
Skewness-0.78217938
Sum628.31
Variance0.094813357
MonotonicityNot monotonic
2024-05-28T14:56:26.983681image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3.4 20
 
10.3%
3.15 14
 
7.2%
3.03 14
 
7.2%
3.39 13
 
6.7%
3.23 13
 
6.7%
3.29 13
 
6.7%
2.64 11
 
5.7%
3.46 8
 
4.1%
3.27 6
 
3.1%
3.19 6
 
3.1%
Other values (26) 76
39.2%
ValueCountFrequency (%)
2.07 1
 
0.5%
2.19 2
 
1.0%
2.36 1
 
0.5%
2.64 11
5.7%
2.68 2
 
1.0%
2.76 1
 
0.5%
2.8 2
 
1.0%
2.87 1
 
0.5%
2.9 3
 
1.5%
3.03 14
7.2%
ValueCountFrequency (%)
4.17 2
 
1.0%
3.9 2
 
1.0%
3.86 1
 
0.5%
3.64 5
2.6%
3.58 5
2.6%
3.54 4
2.1%
3.52 5
2.6%
3.5 5
2.6%
3.47 4
2.1%
3.46 8
4.1%

compression-ratio
Real number (ℝ)

Distinct32
Distinct (%)16.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.244433
Minimum7
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:27.146152image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile7.6
Q18.6
median9
Q39.4
95-th percentile21.935
Maximum23
Range16
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation4.0521875
Coefficient of variation (CV)0.3955502
Kurtosis4.7716781
Mean10.244433
Median Absolute Deviation (MAD)0.4
Skewness2.5348561
Sum1987.42
Variance16.420224
MonotonicityNot monotonic
2024-05-28T14:56:27.343900image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
9 45
23.2%
9.4 26
13.4%
8.5 13
 
6.7%
9.5 13
 
6.7%
8.7 9
 
4.6%
8 8
 
4.1%
9.3 8
 
4.1%
9.2 6
 
3.1%
21 5
 
2.6%
8.4 5
 
2.6%
Other values (22) 56
28.9%
ValueCountFrequency (%)
7 3
 
1.5%
7.5 5
 
2.6%
7.6 4
 
2.1%
7.7 2
 
1.0%
7.8 1
 
0.5%
8 8
4.1%
8.1 2
 
1.0%
8.3 3
 
1.5%
8.4 5
 
2.6%
8.5 13
6.7%
ValueCountFrequency (%)
23 5
2.6%
22.7 1
 
0.5%
22.5 3
1.5%
22 1
 
0.5%
21.9 1
 
0.5%
21.5 4
2.1%
21 5
2.6%
11.5 1
 
0.5%
10.1 1
 
0.5%
10 3
1.5%

horsepower
Real number (ℝ)

Distinct56
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.29381
Minimum48
Maximum288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:27.564118image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum48
5-th percentile62
Q170
median95
Q3116
95-th percentile182
Maximum288
Range240
Interquartile range (IQR)46

Descriptive statistics

Standard deviation39.343858
Coefficient of variation (CV)0.38461619
Kurtosis3.3216119
Mean102.29381
Median Absolute Deviation (MAD)24
Skewness1.572218
Sum19845
Variance1547.9391
MonotonicityNot monotonic
2024-05-28T14:56:27.792181image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68 19
 
9.8%
70 11
 
5.7%
69 10
 
5.2%
95 9
 
4.6%
110 8
 
4.1%
116 8
 
4.1%
114 6
 
3.1%
160 6
 
3.1%
62 6
 
3.1%
88 6
 
3.1%
Other values (46) 105
54.1%
ValueCountFrequency (%)
48 1
 
0.5%
52 2
 
1.0%
55 1
 
0.5%
56 2
 
1.0%
58 1
 
0.5%
60 1
 
0.5%
62 6
 
3.1%
64 1
 
0.5%
68 19
9.8%
69 10
5.2%
ValueCountFrequency (%)
288 1
 
0.5%
262 1
 
0.5%
207 3
1.5%
200 1
 
0.5%
184 2
 
1.0%
182 3
1.5%
176 2
 
1.0%
175 1
 
0.5%
162 2
 
1.0%
160 6
3.1%

peak-rpm
Real number (ℝ)

Distinct23
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5127.0619
Minimum4150
Maximum6600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:27.972260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum4150
5-th percentile4232.5
Q14800
median5200
Q35500
95-th percentile6000
Maximum6600
Range2450
Interquartile range (IQR)700

Descriptive statistics

Standard deviation486.45075
Coefficient of variation (CV)0.094879048
Kurtosis-0.0053809673
Mean5127.0619
Median Absolute Deviation (MAD)300
Skewness0.052314144
Sum994650
Variance236634.33
MonotonicityNot monotonic
2024-05-28T14:56:28.157294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5500 37
19.1%
4800 34
17.5%
5000 23
11.9%
5200 21
10.8%
5400 13
 
6.7%
6000 9
 
4.6%
5250 7
 
3.6%
4500 7
 
3.6%
5800 6
 
3.1%
4150 5
 
2.6%
Other values (13) 32
16.5%
ValueCountFrequency (%)
4150 5
 
2.6%
4200 5
 
2.6%
4250 3
 
1.5%
4350 4
 
2.1%
4400 3
 
1.5%
4500 7
 
3.6%
4650 1
 
0.5%
4750 4
 
2.1%
4800 34
17.5%
4900 1
 
0.5%
ValueCountFrequency (%)
6600 2
 
1.0%
6000 9
 
4.6%
5900 3
 
1.5%
5800 6
 
3.1%
5750 1
 
0.5%
5600 1
 
0.5%
5500 37
19.1%
5400 13
 
6.7%
5300 1
 
0.5%
5250 7
 
3.6%

city-mpg
Real number (ℝ)

Distinct29
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.484536
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:28.348547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile16
Q119.25
median25
Q330.75
95-th percentile37.35
Maximum49
Range36
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.6076003
Coefficient of variation (CV)0.25927881
Kurtosis0.5031065
Mean25.484536
Median Absolute Deviation (MAD)6
Skewness0.59107861
Sum4944
Variance43.660381
MonotonicityNot monotonic
2024-05-28T14:56:28.554242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
31 28
14.4%
19 21
10.8%
24 19
 
9.8%
27 14
 
7.2%
17 13
 
6.7%
26 12
 
6.2%
23 12
 
6.2%
21 8
 
4.1%
25 8
 
4.1%
30 8
 
4.1%
Other values (19) 51
26.3%
ValueCountFrequency (%)
13 1
 
0.5%
14 2
 
1.0%
15 3
 
1.5%
16 6
 
3.1%
17 13
6.7%
18 3
 
1.5%
19 21
10.8%
20 1
 
0.5%
21 8
 
4.1%
22 4
 
2.1%
ValueCountFrequency (%)
49 1
 
0.5%
47 1
 
0.5%
45 1
 
0.5%
38 7
3.6%
37 6
3.1%
36 1
 
0.5%
35 1
 
0.5%
34 1
 
0.5%
33 1
 
0.5%
32 1
 
0.5%

highway-mpg
Real number (ℝ)

Distinct30
Distinct (%)15.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.056701
Minimum16
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:28.749338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q125
median31
Q336.75
95-th percentile43
Maximum54
Range38
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation6.9343241
Coefficient of variation (CV)0.22327948
Kurtosis0.40722585
Mean31.056701
Median Absolute Deviation (MAD)6
Skewness0.46479011
Sum6025
Variance48.084851
MonotonicityNot monotonic
2024-05-28T14:56:28.949536image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
25 19
 
9.8%
38 17
 
8.8%
32 16
 
8.2%
30 15
 
7.7%
34 14
 
7.2%
37 13
 
6.7%
28 12
 
6.2%
33 9
 
4.6%
29 9
 
4.6%
24 9
 
4.6%
Other values (20) 61
31.4%
ValueCountFrequency (%)
16 2
 
1.0%
17 1
 
0.5%
18 2
 
1.0%
19 2
 
1.0%
20 2
 
1.0%
22 8
4.1%
23 7
 
3.6%
24 9
4.6%
25 19
9.8%
26 3
 
1.5%
ValueCountFrequency (%)
54 1
 
0.5%
53 1
 
0.5%
50 1
 
0.5%
47 2
 
1.0%
46 2
 
1.0%
43 4
 
2.1%
42 3
 
1.5%
41 3
 
1.5%
39 2
 
1.0%
38 17
8.8%

price
Real number (ℝ)

Distinct175
Distinct (%)90.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13089.34
Minimum5118
Maximum45400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2024-05-28T14:56:29.163960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum5118
5-th percentile6156.1
Q17775
median10096.5
Q316500
95-th percentile33053
Maximum45400
Range40282
Interquartile range (IQR)8725

Descriptive statistics

Standard deviation8068.6413
Coefficient of variation (CV)0.61642842
Kurtosis3.1864612
Mean13089.34
Median Absolute Deviation (MAD)3133
Skewness1.8365138
Sum2539332
Variance65102973
MonotonicityNot monotonic
2024-05-28T14:56:29.397033image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10295 5
 
2.6%
8495 2
 
1.0%
18150 2
 
1.0%
8921 2
 
1.0%
8845 2
 
1.0%
7609 2
 
1.0%
6692 2
 
1.0%
6229 2
 
1.0%
7957 2
 
1.0%
5572 2
 
1.0%
Other values (165) 171
88.1%
ValueCountFrequency (%)
5118 1
0.5%
5151 1
0.5%
5195 1
0.5%
5348 1
0.5%
5389 1
0.5%
5399 1
0.5%
5499 1
0.5%
5572 2
1.0%
6095 1
0.5%
6189 1
0.5%
ValueCountFrequency (%)
45400 1
0.5%
41315 1
0.5%
40960 1
0.5%
37028 1
0.5%
36880 1
0.5%
36000 1
0.5%
35550 1
0.5%
35056 1
0.5%
34184 1
0.5%
34028 1
0.5%

Interactions

2024-05-28T14:56:15.111492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:35.978923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:38.769067image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:41.737742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:45.576385image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:48.370771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:51.070236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:53.702054image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:56.299893image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:58.584960image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:00.937154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:03.139509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:05.510994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:08.154704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:10.356640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:12.726036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:15.263457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:36.129125image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:38.957562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:41.959150image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:45.744933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:48.552304image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:51.218940image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:53.823143image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:56.438522image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:58.727089image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:01.066505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:03.282954image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:05.652559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:08.285383image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:10.500407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:12.856682image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:15.405357image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:36.348599image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:39.147579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:42.165598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:45.931106image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:48.759432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:51.373079image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:53.978204image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:56.589117image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:58.889472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:01.215322image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:03.423805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:05.799167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:08.417030image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:10.655251image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:13.015645image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:15.573904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:36.541781image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:39.340061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:42.527587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:46.130570image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:48.960299image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:51.516413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:54.132273image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:56.724757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:59.026718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:01.346256image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:03.587472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:05.931811image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:08.572613image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:10.796790image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:13.179948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:15.739567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:36.710329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:39.541169image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:42.769431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:46.295726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:49.179732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:51.676636image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:54.293834image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:56.887247image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:59.186641image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:01.505583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:03.745040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:06.082408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:08.722264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:10.951378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:13.323856image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:15.897332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:36.869907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:39.729175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:43.036766image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:46.481749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:49.370222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:51.819685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:54.424486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:57.033008image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:59.329878image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:01.648302image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:03.898628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:06.229403image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:08.848205image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:11.098475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:13.465778image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:16.036616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:37.088318image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:39.924655image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:43.205234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:46.679223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:49.556723image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:51.944882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:54.578086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:57.163163image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:59.461525image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:01.777956image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:04.034497image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:06.358220image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:08.988635image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:11.244810image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:13.620421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:16.189451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:37.272639image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:40.088805image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:43.348187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:46.861235image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:49.727419image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:52.091649image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:54.709531image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:57.307432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:59.619561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:01.916355image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:04.183486image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:06.503633image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:09.111307image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:11.370600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:13.767083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:16.341051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:37.440192image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:40.237313image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:43.653259image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:47.035768image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:49.863159image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:52.245090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:54.836193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:57.440718image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:59.765341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:02.049999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:04.336577image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:06.640324image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:09.250412image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T14:56:13.893523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T14:55:37.624969image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:40.414199image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:55:43.861300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T14:56:09.797131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T14:55:50.907323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
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2024-05-28T14:56:02.987161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:05.360294image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:08.000206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:10.210000image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:12.557646image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-05-28T14:56:14.945224image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-05-28T14:56:17.798405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-28T14:56:18.347441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
03115.0alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.0111.05000.0212713495.0
13115.0alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.689.0111.05000.0212716500.0
21115.0alfa-romerogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.479.0154.05000.0192616500.0
32164.0audigasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.4010.0102.05500.0243013950.0
42164.0audigasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.408.0115.05500.0182217450.0
52115.0audigasstdtwosedanfwdfront99.8177.366.353.12507ohcfive136mpfi3.193.408.5110.05500.0192515250.0
61158.0audigasstdfoursedanfwdfront105.8192.771.455.72844ohcfive136mpfi3.193.408.5110.05500.0192517710.0
71115.0audigasstdfourwagonfwdfront105.8192.771.455.72954ohcfive136mpfi3.193.408.5110.05500.0192518920.0
81158.0audigasturbofoursedanfwdfront105.8192.771.455.93086ohcfive131mpfi3.133.408.3140.05500.0172023875.0
90115.0audigasturbotwohatchback4wdfront99.5178.267.952.03053ohcfive131mpfi3.133.407.0160.05500.0162210295.0
symbolingnormalized-lossesmakefuel-typeaspirationnum-of-doorsbody-styledrive-wheelsengine-locationwheel-baselengthwidthheightcurb-weightengine-typenum-of-cylindersengine-sizefuel-systemborestrokecompression-ratiohorsepowerpeak-rpmcity-mpghighway-mpgprice
195-174.0volvogasstdfourwagonrwdfront104.3188.867.257.53034ohcfour141mpfi3.783.159.5114.05400.0232813415.0
196-2103.0volvogasstdfoursedanrwdfront104.3188.867.256.22935ohcfour141mpfi3.783.159.5114.05400.0242815985.0
197-174.0volvogasstdfourwagonrwdfront104.3188.867.257.53042ohcfour141mpfi3.783.159.5114.05400.0242816515.0
198-2103.0volvogasturbofoursedanrwdfront104.3188.867.256.23045ohcfour130mpfi3.623.157.5162.05100.0172218420.0
199-174.0volvogasturbofourwagonrwdfront104.3188.867.257.53157ohcfour130mpfi3.623.157.5162.05100.0172218950.0
200-195.0volvogasstdfoursedanrwdfront109.1188.868.955.52952ohcfour141mpfi3.783.159.5114.05400.0232816845.0
201-195.0volvogasturbofoursedanrwdfront109.1188.868.855.53049ohcfour141mpfi3.783.158.7160.05300.0192519045.0
202-195.0volvogasstdfoursedanrwdfront109.1188.868.955.53012ohcvsix173mpfi3.582.878.8134.05500.0182321485.0
203-195.0volvodieselturbofoursedanrwdfront109.1188.868.955.53217ohcsix145idi3.013.4023.0106.04800.0262722470.0
204-195.0volvogasturbofoursedanrwdfront109.1188.868.955.53062ohcfour141mpfi3.783.159.5114.05400.0192522625.0